ECE Seminar with Xi Chen

Learning from Big Data: Scalability & Structures
With Xi Chen
Ph.D. Candidate
Carnegie Mellon University
Faculty Host: Prakash Ishwar
Refreshments will be served outside Room 339 at 3:45 p.m.
Abstract: The development of modern technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray and proteomics, climatological data, and social network data, to name a few. To learn from large-scale and complex data, traditional machine learning techniques either suffer from unaffordable computational costs or are unable to model the complex intrinsic structures latent in data.
To facilitate big data analysis, we need both advanced computational methods to address scalability as well as new statistical models to extract hidden structures from the data. In this talk, Xi Chen will present two research threads to address challenges from both computational and statistical aspects in modern data analysis: (1) A uniformly-optimal stochastic first-order method for large-scale online prediction and its implementation in a distributed environment. (2) A computationally efficient method for predicting dynamic graphical models from complex data. Chen will also talk about the applications of the proposed methods, such as text mining, computational biology and climate data analysis.
About the Speaker: Xi Chen is now completing his Ph.D. in the Machine Learning Department at Carnegie Mellon University. He is developing fast and scalable algorithms for parametric and non-parametric structured sparse learning problems with applications to text mining, computational biology and climate modeling. He also investigates machine learning foundations for collective intelligence, in particular, crowdsourcing. Before his Ph.D., he obtained his master’s degree in Industry Administration and Operations Research from the Tepper School of Business at CMU. He was the recipient of an IBM Ph.D. Fellowship and American Statistical Association (ASA) Student Paper Competition Award. He also has interned in several world-leading research labs, including Microsoft Research, IBM Research and NEC-Research Lab.